Facial Recognition for Disease Diagnosis Using a Deep Learning Convolutional Neural Network: A systematic Review and Meta-Analysis

Abstract

Objective: This study aimed to systematically review the literature on facial recognition technology based on deep learning networks in disease diagnosis over the past ten years to identify the objective basis of this application. Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on November 13, 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past ten years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software. Results: The study collected 22 articles with a total sample size of 57,539 cases, of which 43,301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)]. Conclusion: The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.